Exact Reasoning Under Uncertainty
This work provides a methodological foundation for building expert decision systems in complex domains, though it appears incremental by advocating established probabilistic approaches over newer alternatives.
The paper argues that probabilistic representations and decision-theoretic reasoning are superior to alternatives like fuzzy set theory for expert systems in uncertain environments, and illustrates this with RACHEL, a system for helping infertile couples choose medical treatments.
This paper focuses on designing expert systems to support decision making in complex, uncertain environments. In this context, our research indicates that strictly probabilistic representations, which enable the use of decision-theoretic reasoning, are highly preferable to recently proposed alternatives (e.g., fuzzy set theory and Dempster-Shafer theory). Furthermore, we discuss the language of influence diagrams and a corresponding methodology -decision analysis -- that allows decision theory to be used effectively and efficiently as a decision-making aid. Finally, we use RACHEL, a system that helps infertile couples select medical treatments, to illustrate the methodology of decision analysis as basis for expert decision systems.